Anne-Ruxandra Carvunis, Ph.D.
|PhD, Bioinformatics, Université Joseph Fourier, Grenoble, France|
3501 Fifth Ave., 3064 BST3
What makes each species unique? My research aims at understanding the molecular mechanisms of change and innovation by examining systems biology in the light of evolution and evolution in the light of systems biology.
Systems biology is the study of biological networks. The information contained in the genome of every living cell encodes a specific set of biomolecules (eg. transcripts, proteins). These biomolecules interact with each other, with the genome and with the environment, forming intricate and dynamic networks that underlie all cellular processes. Biological networks define how organisms look and behave, whether they will die or thrive in different environments. Ultimately, biological networks influence the probability that genomic information will be propagated to the next generation. Thus I believe that studying networks will transform how we think about evolution.
Evolution is the process through which populations and species change over successive generations. We know a lot about how natural selection and random drift together govern the inheritance of genetic material. However, the mechanisms underpinning evolutionary innovation remain obscure. How do new genes appear? How do organisms adapt to changing environments? If biological networks performed their functions in the manner of predictable machines, they could not evolve. There must be organizational principles that make biological networks plastic and robust for evolutionary innovation to take place. I seek to discover what these principles are. Through this quest I hope to expand knowledge of how cells work and of how evolution works.
Ongoing work in the lab combines small and large-scale experiments with bioinformatics approaches to describe, engineer and predict the genetic and network-level determinants of species-specificity.
Domazet-Loso T, Carvunis AR, M. Mar, Sestak MS, Bakaric R, Neme R, Tautz D (2017) No evidence for phylostratigraphic bias impacting inferences on patterns of gene emergence and evolution Molecular Biology and Evolution. 34(4): 843-856
Carvunis AR, Wang T, Skola D, Yu A, Chen J, Kreisberg J, Ideker T (2015) Evidence for a common evolutionary rate in metazoan transcriptional networks ELife. 4: e11615
Carvunis AR, Ideker T (2014) Siri of the Cell - what biology could learn from the iPhone Cell. 57(3): 534-8